Install Python 3.10 or newer, Use VS Code with LLM-centric extensions, Set up Git and GitHub for version control, Configure virtual environments for isolated development

Compare Virtualenv (lightweight), Anaconda (data-heavy), and Poetry (modern Python), Get recommendations based on use case

Register and create a project on OpenAI, Generate and store API keys securely, Review pricing tiers and quotas, Implement API access using .env and Python SDK

Activate APIs via Google Cloud Console, Generate OAuth/service account credentials, Manage billing alerts and quotas, Compare Gemini with OpenAI

Use python-dotenv package, Hide .env in .gitignore, Structure keys for multi-API support

Install Ollama on macOS/Linux, Download models like Mistral, LLaMA, Gemma, Run prompts locally for offline development

Prompt engineering (zero-shot, few-shot), Data preprocessing and model evaluation, Fine-tuning and prompt chaining

Compare GPT (general purpose), Claude (long context), and open-source models, Evaluate performance, licensing, and use cases

Craft prompts for clarity/control, Optimize inference with truncation and sampling, Run tasks offline with reduced latency

Tokenization, stemming, lemmatization, Named Entity Recognition (NER), Sentence segmentation and POS tagging

Tokens as model-readable chunks, Embeddings as high-dimensional vectors, Cosine similarity and vector search basics

Summarize long text inputs, Measure speed, accuracy, and cost, Compare cloud vs. local inference

Sketch UI mockups/charts in real-time, Generate HTML/CSS layouts, Visualize logic/flowcharts rapidly

Assess hallucinations, factual consistency, Compare Claude, GPT, Mistral qualitatively

Understand token limits (GPT-4: 8K/32K, Claude: 100K+), Use summarization/chunking to stay within limits

Compare pay-per-token vs. flat monthly plans, Estimate real-world costs

Prompt templates for JSON/YAML/XML, Validate outputs with Pydantic/json.loads()

Explore API syntax and capabilities, Handle prompt formatting and token limits, Build apps with real-time interaction

Use OpenAI/Anthropic streaming endpoints, Implement async streaming for live feedback

Test models under stress with adversarial prompts, Detect hallucinations/bias and improve robustness

Understand attention, layers, embeddings, Learn how LLaMA/Qwen implement transformers

Create web frontends for LLMs in minutes, Build text boxes, buttons, and streaming outputs

Add feedback loops and session memory, Style apps for professional use

Display AI responses as they generate, Handle stop tokens and retries

Route prompts to GPT/Claude dynamically, Compare responses side-by-side

Integrate OpenAI API with memory, Deploy with authentication and multi-turn context

Detect user intent, Generate context-aware responses, Log conversations for improvement

Store/pass conversation history, Handle interruptions and topic switching

Use few-shot prompting for dynamic replies, Inject user history to refine accuracy

Connect code interpreters to LLM apps, Run math/processing in sandboxed environments

Integrate web search, databases, or APIs, Build agents that talk to tools

Design memory and tool usage for flights/dates, Build an MVP booking flow

Define tools with JSON schemas, Parse and route output to tool execution

Combine memory, context, and function-calling, Deploy on web with Gradio/Flask

Use GPT + DALL-E for visual generation, Integrate Whisper for speech-to-text

Generate visuals from prompts, Prototype in JupyterLab for slides/UI

Combine LLMs, voice models, and vision APIs, Handle multi-format inputs/outputs

Browse models/datasets on the Hub, Understand model licenses and community contributions

Search/filter models using Leaderboard, Explore Spaces for live demos

Set up GPU/TPU for inference, Save/share notebooks

Store API tokens securely in .env/Colab secrets, Load models/datasets directly in Colab

Load models like LLaMA, Mistral with transformers, Scale with quantization (bitsandbytes)

Simplify tasks like summarization/translation, Reduce boilerplate with high-level APIs

Customize model configuration, Switch from CPU to GPU seamlessly

Compare tokenizers (LLaMA, PHI, Starcoder), Optimize prompts by minimizing token count

Modify pre-tokenization settings, Create domain-specific vocabularies

Fine-tune control over input/output, Understand logits and attention outputs

Load models with 4-bit quantization, Save memory for limited hardware

Load a fine-tuned model for jokes, Test randomness and humor quality

Build workflows with tokenizers/models, Follow best practices for deployment

Transcribe audio using Whisper, Summarize text with Gemini/LLMs

Transcribe/summarize meetings, Generate action points